International Journal of Machine Learning and Computing, Vol. 3, No. 2, April 2013
A Recursive TF-ISF Based Sentence Retrieval Method with
Local Context
Alen Doko, Maja Štula, and Darko Stipaničev
TF-ISF method was also modified to take into account
context, but unsuccessfully without statistically significant
improvements [1]. Fernandez sees the reason for using
context of sentences in the vocabulary mismatch problem
rooted in the sparsity of sentences [1]. He also thinks that the
context of sentences contains a content that is not encoded in
the sentence, but that is related to the sentence and it is
important not just because of vocabulary mismatch. In other
words the topic of the sentence is spread across the sentence
and the context of the sentence. We agree that it is important
to take into account local context of sentences if we want to
develop good sentence retrieval methods. We especially
think that it is valuable to improve the TF-ISF baseline which
showed good results in the past [6], [8], [10] with the
sentence’s context. For that reason we make a new attempt to
improve the TF-ISF method, explained in Section III to take
into account the context of sentences.
Abstract—Sentence retrieval consists of retrieving relevant
sentences from a document base in response to a query.
Question answering, novelty detection, summarization, opinion
mining and information provenance make use of sentence
retrieval. Most of the sentence retrieval methods are trivial
adaptations of document retrieval methods. However some
newer sentence retrieval methods based on the language
modeling framework successfully use some kind of context of
sentences. Unlike that there is no successful improvement of the
TF-ISF method that takes into account the context of sentences.
In this paper we propose a recursive TF-ISF based method that
takes into account the local context of a sentence. The context is
considered the previous and next sentence of current sentence.
We compared the new method to the TF-ISF baseline and to an
earlier unsuccessful method that also incorporates a similar
context into TF-ISF. We got statistically significant
improvements of the results in comparison to both of the
methods. Additional benefit of our method is the clear explicit
model of the context that will allow us to automatically generate
a document representation with context suitable for sentence
retrieval which is important for our future work.
II. RELATED WORK
Index Terms—Context, document representation, TF-ISF,
sentence retrieval, recursion.
Sentence retrieval methods are usually simple adaptations
of document retrieval methods where sentences are treated as
documents [3]-[5]. One of the first and most successful
methods for sentence retrieval was the TF-ISF method [6]
which is a trivial adaptation of the TF-IDF method for
document retrieval. This method was shown to outperform
other methods like BM25 based methods or language
modeling based methods [6], [8], [10]. Another typically
used method for sentence retrieval is the query likelihood
method which is a language modeling approach to document
retrieval. That method, invented by Ponte & Croft for
document retrieval [11] was in a trivial manner adapted and
often used for sentence retrieval [2]. The TF-ISF and the
query likelihood method can be considered baseline methods
for sentence retrieval. The TF-ISF method and the query
likelihood method were used as the baseline methods for
sentence retrieval in [1] and the query likelihood method was
used as the baseline method for sentence retrieval in [2].
In the recent works [1], [2] query likelihood was modified
to take into account the local context of the sentence. Due to
the sparsity of the sentences there is little overlap between the
query and the sentence which negatively affects the
performance of sentence retrieval [1]. The assumption is that
this problem can be partially solved by using the local context
of sentences.
The idea that “good” sentences come from “good”
documents was proposed by Murdock [2]. So the query
likelihood method was improved using local context in form
of the document the sentence came from. A mixture model
was proposed which combines a sentence language model,
document language model and collection language model.
I. INTRODUCTION
Sentence retrieval is the task of finding relevant sentences
from a document base in response to a query. Tasks like
novelty detection, question answering, summarization,
opinion mining and information provenance make use of
sentence retrieval [1], [2]. Sentence retrieval methods are
usually simple adaptations of document retrieval methods
where sentences are treated as documents [3]-[5]. The state of
the art and most successful models for sentence retrieval are
the vector space model [1], [6], [7] and the language model
approach with local context [1], [2]. The vector space model
for sentence retrieval uses the term frequency - inverse
sentence frequency method (TF-ISF) which is analogous to
the traditional term frequency – inverse document frequency
method (TF-IDF) used for document retrieval. When it
comes to the language model approach for sentence retrieval
at first it was implemented as a trivial adaptation from
document retrieval to sentence retrieval, with sentences
treated as documents [7]. In the recent works language model
approach was successfully modified to take into account the
context of sentence, like the document which contains it or
some number of previous or next sentences [1], [2], [8]. The
Manuscript received January 5, 2013; revised April 19, 2013.
A. Doko is with the JP Croatian Telecommunications d.o.o. Mostar,
Mostar, Bosnia and Herzegovina (e-mail: [email protected])
M. Štula and D. Stipaničev are with the Faculty of Electrical Engineering,
Mechanical Engineering and Naval Architecture, University of Split, Split,
Croatia (e-mail: [email protected]; [email protected]).
DOI: 10.7763/IJMLC.2013.V3.301
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International Journal of Machine Learning and Computing, Vol. 3, No. 2, April 2013
The method showed better results when compared to the
query likelihood baseline [2].
A sentence local context was also incorporated into the
language modeling framework to better estimate the
relevance of a sentence by Fernandez et. al. [1]. The
document that contains the sentence and surrounding
sentences (previous, current and next sentence) were used as
the local context. Additionally, the importance of a sentence
within a document was used. Tests showed significant
improvements of language modeling methods when using
local context in comparison to baselines like TF-ISF, and
BM25 and to language modeling methods that ignore local
context. Including sentence importance additionally
improved the performance of tested language modeling
approaches.
Fernandez et. al. also tried to improve the TF-ISF method
by modifying it to take into account the local context [1].
Two types of local context, document that contains the
sentence and surrounding sentences (previous, current and
next sentence), were tested again. They tried to modify the
TF part to take into account the number of occurrences of
term in the context and also tried to compute the ISF part at
document level rather than at sentence level. The tests did not
show consistent and significant improvements.
A local context, implemented as a sliding window, was
also used in [12] for the task of information retrieval. A
sliding window consisted of multiple sentences. The whole
sliding window was compared to the topic and if the sliding
window is relevant all sentences inside it are considered
relevant. The retrieval method was based on comparing
nouns and verbs in the sliding window and the topic. Some
tests showed best result when the sliding window is of size 4
but no comparisons were made to the state of the art methods.
( | )=∑
∈
log (
,
+ 1)log (
,
+ 1)log
.
(1)
where
•
•
•
•
is the number of occurences of term t in question q
is
the number of occurrences of term t in sentence s
,
number of sentences that contain term t
number of sentences in the collection
,
As the local context of each sentence we are using the
previous and next sentence in the same document. We
assume that the relevance of a sentence depends partly on the
content of the sentence itself and partly on the content of the
two neighboring sentences. The relevance of the neighboring
sentences again depends partly on their neighbor’s content.
Using these two assumptions we define the new recursive
ranking function for sentence retrieval as follows:
∙
( | ) = (1 − ) ∙ ( | ) +
(
( )
+
( )| )
(2)
( ) depicts previous sentence of sentence and
where
( ) next sentence of sentence .
( )
and
(
( )| ) represent the relevance of the previous and
next sentence.
( )
is by definition 0 if is first
(
sentence in document and
( )| ) is by definition 0
if s is last sentence in document. is a tuning parameter. In
our tests in chapter 4 the recursive function calls itself until
three previous and three next sentences of the sentence are
involved. In other words three recurrences are used. After
( | ) = ( | ). Of course it
that no context is used i.e.
is possible to take into account all the neighboring sentences
in a document. Tests, showing if it can help to include more
than three previous and three next sentences into computation
of the relevance of a sentence, are left for future work.
One benefit of our TF-ISF method in comparison to the
tfmix method from [1] is the explicit modeling of the
relevance of the context (previous and next sentence) to the
(
query
( )
+
( )| ) while in [1]
parts of the TF-ISF function are replaced with components
related to the sentence and to the neighbor sentences ( , is
replaced with ∙ , + (1 − ) ,
where
is a
tuning parameter). We think that our approach is better
because it allows better exploring of the influence of context
to the sentence relevance. That is important for our future
work where we want to automatically generate a document
representation for sentence retrieval. Our function
( | )) can be used to automatically create a useful
(
structured representation of a textual document with a
representation of context. Our aim is to use such
representations in a web environment where structured
document representations are stored at web servers along
with the plain text documents. That means that a part of the
computation needed for sentence retrieval will be converted
to a document representation.
Second benefit of our TF-ISF method are statistically
significant improvements according MAP, R-precision and
P@100 in comparison to the baseline presented in chapter 4
while the tfmix method did not show statistically significant
improvements [1].
III. TF-ISF WITH LOCAL CONTEXT
From the previous examples we saw that several sentence
retrieval methods were improved by using of local context of
sentences. One exception is the TF-ISF method. In this paper
we show that it is possible to improve the TF-ISF method by
using local context that consists of the neighboring sentences
(previous and next sentence of the current sentence) and
using a recursive ranking function. While in [1] it was tried to
include the local context by modifying parts of the TF-ISF
ranking function we are extending it with the estimate of the
relevance of the context to the query. It is important to notice
that inside the scope of this paper our aim is to show that it is
possible to improve the TF-ISF method using local context
which was not successful in [1]. In other words we will show
improvement of the new method in comparison to the
baseline TF-ISF method. As additional evidence we will also
compare the new method to the tfmix method presented in [1]
that, similarly to our method, tries to improve TF-ISF using
surrounding sentences. Tests that show weather our new
TF-ISF method with context can outperform other sentence
retrieval methods with local context (e.g. language modeling
methods with context) are left for future work.
A. Definition of the Ranking Function
The TF-ISF based ranking function for sentence retrieval
is [6], [9]:
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International Journal of Machine Learning and Computing, Vol. 3, No. 2, April 2013
IV. EXPERIMENT
We used short queries from the title field. We measured
the performance using a series of P@X measures (including
the often used P@10) and the standard measures MAP, and
R-precision. To compare the difference between two
methods we used paired t-test with significance level
= 0.05.
Our ranking function requires tuning of the parameter , so
we employed a train-test methodology similar to [1]. We
experimented with three training-testing configurations using
TREC Novelty track data as follows:
We tested our new method for sentence retrieval using data
from the TREC Novelty tracks. There were three TREC
Novelty Tracks in the years from 2003 to 2004 [3], [4], [5].
The task was novelty detection which consists of two
subtasks, finding relevant sentences and finding novel
sentences. We are only interested in finding relevant
sentences which is called sentence retrieval. Sentence
retrieval is important part of novelty detection e.g. Allan [6]
showed that the performance of novelty detection depends on
the quality of the performance of sentence retrieval.
In each of the three Novelty Tracks in the years 2002, 2003
and 2004 the task was as follows: given a topic and an
ordered list of documents find relevant and novel sentences.
In each Track participants got a set of 50 topics where each
topic consisted of titles, descriptions and narratives. They
also got a list of mostly relevant documents and a list of
sentence level relevance judgments.
In TREC 2002 the topics from ad hoc Tracks were used.
To each topic 25 documents were assigned. If the topic had
25 or more relevant documents only 25 relevant documents
were used. If the topic had less than 25 documents
non-relevant documents were added to reach the number of
25 documents. The assessors marked about 2% of the
sentences relevant.
In TREC 2003 topics where constructed specially for the
Novelty track. For every topic 25 relevant documents were
chosen. 37.56% of sentences were judged relevant.
In TREC 2004 between 25 and 100 documents were
chosen with 25 of them relevant. 16.2% of sentences were
judged relevant.
An example of a topic from the TREC 2002 Novelty track
is shown in Table I.
• Training with TREC 2002 and testing with TREC 2003
and TREC 2004
• Training with TREC 2003 and testing with TREC 2002
and TREC 2004
• Training with TREC 2004 and testing with TREC 2002
and TREC 2003
Training was performed to find the value of parameter
for which the system shows best performance. During each of
the tree trainings (TREC 2002, 2003, 2004) we tried values
from = 0.0 to = 1.0 in steps of 0.05. The best value of
was fixed in order to apply it to the two remaining data sets.
During training we measured the performance of the system
by using Mean average precision (MAP). Table II. shows the
optimal parameter values for the three datasets.
TABLE II: OPTIMAL VALUES OF TF-ISFCON METHOD
Description
Narrative
International Art Crime
Isolate instances of fraud or embezzlement in the
international art trade.
A relevant document is any report that identifies an instance
of fraud or embezzlement in the international buying or
selling of art objects. Objects include paintings, jewelry,
sculptures and any other valuable works of art. Specific
instances must be identified for a document to be relevant;
generalities are not relevant.
2
0.1
TREC 2004
0.2
TABLE III: OPTIMAL
We assume that it is possible to improve the TF-ISF
method for sentence retrieval by taking into account the local
context of each sentence that consists of the previous (left)
sentence and the next (right) sentence (see equation (2)).
To evaluate our new method we compare the baseline
TF-ISF method (with ranking function ( | )) and the new
TF-ISFcon method we propose (with ranking function
( | )). As additional evidence we compare the new
TF-ISFcon with the tfmix method from [1].
In the experiments we partially used Rapidminer 1 , an
open-source system for data mining with Text Extension 2
that allows using the vector space model. With Rapidminer
all upper cases were transformed to lower case, standard stop
words were removed. Stemming was not applied.
1
0.2
TREC 2003
The training of the tfmix method [1] was performed
identical to TF-ISFcon (values from = 0.0 to = 1.0 in
steps of 0.05 were tested). Table III shows optimal parameter
values for the three datasets.
TABLE I: EXAMPLE OF A TOPIC FROM THE TREC 2002 NOVELTY TRACK
Title
TREC 2002
VALUES OF THE TFMIX METHOD
TREC 2002
0.7
TREC 2003
0.85
TREC 2004
0.6
The next tables (Table IV, V. and VI) show the results for
the three training-testing configurations. Statistically
significant differences between the TF-ISFcon and the
baseline TF-ISF and between the tfmix and the baseline
TF-ISF are marked with an asterisk. Statistically significant
differences between the TF-ISFcon and tfmix are marked with
a †. When it comes to the MAP the tests show significant
improvement when using TF-ISFcon in comparison to the
baseline TF-ISF and tfmix for each of the tested data sets and
values of . Also, TF-ISFcon shows significant improvement
for most of the data sets and values of (5 out of 6) according
R-Precision. When it comes to the TF-ISFcon and P@x
measures we had altogether 48 measurements with 4
statistically significant improvements (according to P@100)
and 44 not statistically significant differences. The tfmix
method showed similar performance as in [1] with no
statistically significant improvements of the baseline.
http://rapid-i.com/content/view/181/196/
http://rapid-i.com/content/view/202/206/
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TABLE IV: P@X, MAP AND R-PRECISION FOR TREC 2003 AND 2004,
TREC 2003
= 0.2,
= 0.7
TREC 2004
TF-ISF
tfmix
TF-ISFcon
TF-ISF
tfmix
TF-ISFcon
P@5
0.7160
0.7200
0.7200
0.4360
0.4440
0.4560
P@10
0.6980
0.7040
0.6980
0.4220
0.4460
0.4460
P@15
0.7040
0.6960
0.7000
0.4280
0.4253
0.4320
P@20
0.6890
0.6880
0.6980
0.4260
0.4240
0.4230
P@25
0.6800
0.6760
0.6864
0.4168
0.4160
0.4176
P@30
0.6707
0.6640
0.6747
0.4113
0.4160
0.4147
P@50
0.6436
0.6496
0.6556
0.4040
0.3972
0.4028
P@100
0.6078
0.6112
0.6184*†
0.3660
0.3650
0.3716
MAP
0.5764
0.5775
0.5930*†
0.3252
0.3260
0.3398*†
R-Precision
0.5457
0.5481
0.5725*†
0.3376
0.3401
0.3456
TABLE V: P@X, MAP AND R-PRECISION FOR TREC 2002 AND 2004,
TREC 2002
= 0.1,
= 0.85
TREC 2004
TF-ISF
tfmix
TF-ISFcon
TF-ISF
tfmix
TF-ISFcon
P@5
0.3200
0.3360
0.3520
0.4360
0.4400
0.4480
P@10
0.2900
0.3140
0.3020
0.4220
0.4420
0.4340
P@15
0.2973
0.2907
0.2960
0.4280
0.4333
0.4347
P@20
0.2760
0.2820
0.2820
0.4260
0.4220
0.4210
P@25
0.2648
0.2776
0.2752
0.4168
0.4192
0.4232
P@30
0.2587
0.2680
0.2680
0.4113
0.4160
0.4187
P@50
0.2416
0.2416
0.2488
0.4040
0.3996
0.3988
P@100
0.1904
0.1934
0.2146*†
0.3660
0.3672
0.3714
MAP
0.1952
0.1973
0.2315*†
0.3252
0.3258
0.3392*†
R-Precision
0.2414
0.2448
0.2666*†
0.3376
0.3403
0.3473*†
TABLE VI: P@X, MAP AND R-PRECISION FOR TREC 2002 AND 2003,
TF-ISF
TREC 2002
tfmix
TF-ISFcon
TF-ISF
= 0.2,
= 0.6
TREC 2003
tfmix
TF-ISFcon
P@5
0.3200
0.3200
0.3480
0.7160
0.7240
0.7200
P@10
0.2900
0.3140
0.3040
0.6980
0.6860
0.6980
P@15
0.2973
0.3000
0.3000
0.7040
0.6947
0.7000
P@20
0.2760
0.2910
0.2880
0.6890
0.6830
0.6980
P@25
0.2648
0.2784
0.2792
0.6800
0.6800
0.6864
P@30
0.2587
0.2680
0.2727
0.6707
0.6667
0.6747
P@50
0.2416
0.2440
0.2496
0.6436
0.6508
0.6556
P@100
0.1904
0.1934
0.2154*†
0.6078
0.6100
0.6184*†
MAP
0.1952
0.1971
0.2322*†
0.5764
0.5773
0.5930*†
R-Precison
0.2414
0.2451
0.2672*†
0.5457
0.5483
0.5725*†
To achieve a better insight into the performance of the new
method regarding R-precision and P@x measures we put the
data of all three TREC-s together. This time we do not have a
training data set to pick the best value for . For that reason
we report the results for a whole range of values. The results
are shown in Table VII. Again significant differences to the
baseline are marked with an asterisk.
This time we can see improvements for a whole range of
-values ( = 0.1 − 0.3) when it comes to the MAP and
R-Precision. At the same time we do not have significant
differences according the P@x values (except for P@100).
When choosing higher values of ( = 0.4, = 0.5) we
start to get significantly worse results according to some of
the P@x measures and stop getting significantly better results
according to MAP and R-Precision. This scenario was
expected because there must be a threshold value of at
which the influence of the neighboring sentences is too high.
We can see from the Table VII that it is quite easy to find a
value of that improves the baseline according to MAP and
R-Precision and at the same time gives competitive results
according to reported P@x measures. It should be noted that
we also have significant better results according P@100
measures for = 0.1, = 0.2 and = 0.3.
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International Journal of Machine Learning and Computing, Vol. 3, No. 2, April 2013
TABLE VII: P@X, MAP AND R-PRECISION FOR THE COMBINED DATA SETS OF TREC 2002, TREC 2003 AND TREC 2004
TF-ISF
TF-ISFcon
= 0.1
= 0.2
= 0.3
= 0.4
= 0.5
P@5
0.4907
0,5107
0,5080
0,4987
0,4853
0,4600
P@10
0.4700
0,4847
0,4827
0,4787
0,4633
0,4367
P@15
0.4764
0,4782
0,4773
0,4702
0,4547
0,4409*
P@20
0.4637
0,4663
0,4697
0,4657
0,4537
0,4353*
P@25
0.4539
0,4629
0,4611
0,4573
0,4480
0,4291*
P@30
0.4469
0,4549
0,4540
0,4476
0,4431
0,4187*
P@50
0.4297
0,4337
0,4360
0,4353
0,4189
0,3999*
P@100
0.3881
0,4014*
0,4018*
0,3974*
0,3903
0,3762*
MAP
0.3656
0,3885*
0,3883*
0,3850*
0,3771*
0,3634
R-Prec.
0.3749
0,3944*
0,3951*
0,3914*
0,3815
0,3683
The measures MAP and R-precision for which our new
method shows better performance are recall oriented. This is
identical to the improvements of including context into
language modeling framework [1]. Including context
promotes sentences that don’t have many terms in common
with the query but have some terms in common in the context
which increases recall. Recall is important for the application
scenario presented in [3], [1]. In the scenario a user uses a
smart “next” button which allows him to walk down a ranked
list of documents by highlighting only relevant (and novel)
sentences. Having access to all relevant documents is also
important for multi-document summarization [1]. At the
same time when it comes to the reported precision oriented
measures (P@5 – P@100) we have competitive performance
(except for P@100 where we have significant improvements)
so we do not have to worry when performing tasks that
require high precision like question answering.
Another interesting point is that our method also shows
improved performance according to MAP and R-precision,
and P@100 when using even smaller values of than 0.1.
For example we tried = 0.01 and got significantly better
results. The reason may lie in the fact that the baseline
TF-ISF ranking function produces the same scores for
multiple sentences. At such situations additional evidence, no
matter how small it is, from the neighbor sentences can help
to better rank the sentences.
context into TF-ISF presented in [1]) and showed statistically
significant improvements according the MAP and
R-Precision measures and competitive results according a
series of P@x measures. One additional benefit of the new
TF-ISFcon method is that it models explicitly the relevance of
the context to the query which allows better exploration of
the influence of the context to sentence relevance. In future
works we plan to automatically generate a useful structured
representation of a textual document with a representation of
context capable of sentence retrieval and that can be stored on
web servers.
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V. CONCLUSION
At the beginning of this paper we presented the state of
the art and most successful methods for sentence retrieval,
the TF-ISF method and the language modeling approach with
local context. It was stated that it is important to take into
account the local context of sentences because of the word
mismatch problem and additional content that is related to the
sentence. An earlier work tried to improve the TF-ISF
method using some context but without statistically
significant improvement. Therefore, the novel TF-ISFcon
method with a recursive ranking function was proposed that
in a new way and successfully extends the TF-ISF method to
take into account the local context of sentence. Precisely the
local context is considered to be the previous and the next
sentence. The method was compared against the TF-ISF
baseline and the tfmix method (an earlier try to include
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International Journal of Machine Learning and Computing, Vol. 3, No. 2, April 2013
Dr. Štula’s research interests include design, analysis and implementation
of intelligent technologies from areas of multi-agent systems, information
retrieval, qualitative modeling with fuzzy cognitive maps and intelligent
environment protection. She is IEEE member.
Alen Doko was born on 1982 in Mostar, Bosnia and
Herzegovina. He received his Graduate Engineer
Degree in 2007. Since 2007 he has been a Ph.D
student.
He is also a Business and Analytical Information
Systems
Associate
at
JP
Croatian
Telecommunication d.o.o. Mostar, Mostar, Bosnia
and Herzegovina. His research interests include
Semantic Web, Information Retrieval and Sentence
Retrieval. He is author and coauthor of two scientific papers.
Darko Stipaničev is professor of computer science
and automatic control at Faculty of Electrical
Engineering, Mechanical Engineering and Naval
Architecture (FESB), University of Split. He
received his Ph.D. title in Electrical Engineering
from University of Zagreb in 1989.
His current position is senior full professor at
FESB, University of Split from 2002. Previous
positions include researcher position at company
Koncar, Zagreb 1977-1980. From 1981. he is employed by FESB, University
of Split.
He is also Head of center for wildfire research and Chair of department for
Modeling and Intelligent Computer Systems. He is author or coauthor of
more than 100 scientific papers.
Dr. Stipaničev’s research interest include complex systems modeling and
control, intelligent systems analyses and design, digital image analyses,
advanced Internet technologies and recently, the application of ICT in
environmental protection, particularly wildfire prevention and management.
He is IEEE member. More details on http://laris.fesb.hr/dstip-e.html.
Maja Štula is associated professor of computer
science at the Faculty of Electrical Engineering,
Mechanical Engineering and Naval Architecture,
University of Split. She received her B.S.E.E. in
1996. , M.S.E.E. in 2001. and Ph.D. title in 2005, all
from FESB, University of Split.
Her current position is associated professor of
computer science at FESB, University of Split.
Previous positions include companies THT
(Croatian Telecom) and Infin d.o.o. where she worked as system designer
and developer. She is employed by FESB from 1998. She is author or
coauthor of more than 40 scientific papers, mainly in the multi-agent and
fuzzy cognitive map fields.
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